Crop selection in Agri-PV: international review based strategic decision-making model

Abstract

Agri-Photovoltaics (Agri-PV) is well known for its dual land use, integrating solar energy generation with agricultural production. This not only optimizes land use but also enhances food and energy security. Since Agri-PV is closely linked with crop cultivation, it is not solely about energy generation but also requires careful consideration of crop suitability within Agri-PV installations. Despite its significance, there is limited information available to guide decision-making for crop selection in Agri-PV systems. Selecting suitable crops remains a complex challenge, as factors such as shading tolerance, water requirements, and economic viability vary across different geographical and climatic conditions. This study develops a novel, review-based decision support model for crop selection in Agri-PV systems, synthesizing international research and case studies to provide a structured framework for decision-making. The model is based on 12 main crop typologies and key parameters such as water use, shading adaptability, crop yield/economic potential, and space requirements, derived from 117 research articles and case studies from 25 countries. By leveraging insights from successful international implementations, the model provides a practical framework for policymakers, farmers, and energy planners to enhance the sustainability and efficiency of Agri-PV projects. Findings suggest that crop selection strategies must align with regional climate conditions and PV system design to maximize synergies between energy and food production. High-value crops that require less space and have higher shade tolerance are more suitable for small-scale or decentralized Agri-PV systems. Future research should focus on advanced modeling techniques, AI-driven optimization, and real-world pilot studies to further refine decision-making in Agri-PV deployment. This study contributes to the growing body of knowledge on Agri-PV systems by providing a novel crop suitability matrix for effective decision-making.